Prevalence and bidirectional association between rhinitis and urticaria: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Rhinitis, allergic rhinitis in particular, and urticaria are both common diseases globally. However, there is controversy with regard to the correlation between rhinitis and urticaria. Objective: To examine the accurate association between rhinitis and urticaria. Methods: Three medical literature data bases were searched from data base inception until January 11, 2022. The prevalence and association between rhinitis and urticaria were estimated by meta-analysis. Quality assessment was performed by using the Newcastle‐Ottawa Scale. Pooled odds ratios (OR) with 95% confidence intervals (CI) and pooled prevalence were calculated by using random-effects models. Results: Urticaria prevalence in patients with rhinitis was 17.6% (95% CI, 13.2%‐21.9%). The pooled prevalence of rhinitis was 31.3% (95% CI, 24.2%‐38.4%) in patients with urticaria, and rhinitis prevalence in patients with acute urticaria and chronic urticaria was 31.6% (95% CI, 7.4%‐55.8%) and 28.7% (95% CI, 20.4%‐36.9%), respectively. Rhinitis occurrence was significantly associated with urticaria (OR 2.67 [95% CI, 2.625‐2.715]). Urticaria and rhinitis were diagnosed based on different criteria, possibly resulting in a potential error of misclassification. Conclusion: Rhinitis and urticaria were significantly correlated. Physicians should be cognizant with regard to this relationship and address nasal or skin symptoms in patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.040 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".